GenAIExamples/CodeTrans/docker_compose/intel/cpu/xeon/README.md

Build Mega Service of CodeTrans on Xeon

This document outlines the deployment process for a CodeTrans application utilizing the GenAIComps microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution using microservices llm. We will publish the Docker images to Docker Hub soon, it will simplify the deployment process for this service.

🚀 Create an AWS Xeon Instance

To run the example on a AWS Xeon instance, start by creating an AWS account if you don't have one already. Then, get started with the EC2 Console. AWS EC2 M7i, C7i, C7i-flex and M7i-flex are Intel Xeon Scalable processor instances suitable for the task. (code named Sapphire Rapids).

For detailed information about these instance types, you can refer to this link. Once you've chosen the appropriate instance type, proceed with configuring your instance settings, including network configurations, security groups, and storage options.

After launching your instance, you can connect to it using SSH (for Linux instances) or Remote Desktop Protocol (RDP) (for Windows instances). From there, you'll have full access to your Xeon server, allowing you to install, configure, and manage your applications as needed.

🚀 Build Docker Images

First of all, you need to build Docker Images locally and install the python package of it. This step can be ignored after the Docker images published to Docker hub.

1. Build the LLM Docker Image

git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
docker build -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .

2. Build MegaService Docker Image

git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/CodeTrans
docker build -t opea/codetrans:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .

3. Build UI Docker Image

cd GenAIExamples/CodeTrans/ui
docker build -t opea/codetrans-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .

4. Build Nginx Docker Image

cd GenAIComps
docker build -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/nginx/Dockerfile .

Then run the command docker images, you will have the following Docker Images:

• opea/llm-tgi:latest
• opea/codetrans:latest
• opea/codetrans-ui:latest
• opea/nginx:latest

🚀 Start Microservices

Required Models

By default, the LLM model is set to a default value as listed below:

Service	Model
LLM	HuggingFaceH4/mistral-7b-grok

Change the LLM_MODEL_ID below for your needs.

Setup Environment Variables

1. Set the required environment variables:

   # Example: host_ip="192.168.1.1"
   export host_ip="External_Public_IP"
   # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
   export no_proxy="Your_No_Proxy"
   export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
   # Example: NGINX_PORT=80
   export NGINX_PORT=${your_nginx_port}
   

2. If you are in a proxy environment, also set the proxy-related environment variables:

   export http_proxy="Your_HTTP_Proxy"
   export https_proxy="Your_HTTPs_Proxy"
   

3. Set up other environment variables:

   cd GenAIExamples/CodeTrans/docker_compose
   source ./set_env.sh
   

Start Microservice Docker Containers

cd GenAIExamples/CodeTrans/docker_compose/intel/cpu/xeon
docker compose up -d

Validate Microservices

1. TGI Service

   curl http://${host_ip}:8008/generate \
     -X POST \
     -d '{"inputs":"    ### System: Please translate the following Golang codes into  Python codes.    ### Original codes:    '\'''\'''\''Golang    \npackage main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n    '\'''\'''\''    ### Translated codes:","parameters":{"max_new_tokens":17, "do_sample": true}}' \
     -H 'Content-Type: application/json'
   

2. LLM Microservice

   curl http://${host_ip}:9000/v1/chat/completions\
     -X POST \
     -d '{"query":"    ### System: Please translate the following Golang codes into  Python codes.    ### Original codes:    '\'''\'''\''Golang    \npackage main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n    '\'''\'''\''    ### Translated codes:"}' \
     -H 'Content-Type: application/json'
   

3. MegaService

   curl http://${host_ip}:7777/v1/codetrans \
       -H "Content-Type: application/json" \
       -d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n}"}'
   

4. Nginx Service

   curl http://${host_ip}:${NGINX_PORT}/v1/codetrans \
       -H "Content-Type: application/json" \
       -d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n}"}'
   

🚀 Launch the UI

Launch with origin port

Open this URL http://{host_ip}:5173 in your browser to access the frontend.

Launch with Nginx

If you want to launch the UI using Nginx, open this URL: http://{host_ip}:{NGINX_PORT} in your browser to access the frontend.

Here is an example for summarizing a article.